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Q Search Aal 2! | ST_IsleRoyaleWB_2018.pdf (page 20 of 25) SimBio Virtual Labs" | Isle Royale SimBio Virtual Labs" | Isle Royale xtension

Screenshot 2019-05-01 at 9.52.56 PM.pngIt's from SimuBio Lab. Isle Royal. Need solutions for this part of the lab. Thanks

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Aal 2! |
ST_IsleRoyaleWB_2018.pdf (page 20 of 25)
SimBio Virtual Labs" | Isle Royale
SimBio Virtual Labs" | Isle Royale
xtension Exercise: What's the Difference?
In Exercise 2 you conducted an experiment comparing health of moose with wolves absent to
health of moose with wolves present. You probably observed at least a small difference between the
Number of Moose
samples, but does that really indicate that moose have greater fat stores when wolves are present?
The difference could be related to wolves, but it could also have arisen simply by chance. You might
Amount of Fat Stores
have accidentally selected very healthy moose
one time and unhealthy moose the other. How can
you know whether the difference in means between two samples is real?
Which of the above graphs (A, B, or C) would make the most convincing argument that
The short answer is that you can't. But you can make a good guess using statistics. In fact, "inferential
the difference in fat stores is real, and not just due to chance?
statistics" were invented to allow us to better uncover the truth and answer these sorts of questions.
A. Fat stones increased significantly while wolves were present.
In this section, you will perform a simple statistical test, called a t-test, to decide whether or not
[ 2.2 ]
Explain your choice:
the wolves' presence had a significant effect on moose fat stores. If we were to be very thorough
and formal in our t-test lesson, we would include a lengthy discussion of such concepts as random
variables, sampling distributions, standard errors, and alpha levels. These are important, but to keep
this short, we will just focus on the core ideas underlying the t-test.
You start with a question: Is the mean moose fat stores different when wolves are present versus absent?
The null hypothesis is
is no real difference. Under the null hypothesis, the
If there is a lot of variability in the data sets you are comparing, you will more likely see a difference
difference in your samples arises from chance. The alternative hypothesis is that there is an effect of
in their means just by chance, supporting the null hypothesis. Only if the difference in means is large
wolves on moose fat stores. In order to know which hypothesis your samples support, we examine
compared to the amount of variability in the data do you suppose that the difference might be real. A
the difference in means relative to the variability you observed.
statistic called : formalizes this intuition - in fact, ? is calculated as a ratio of 'difference in means' to
amount of variability'. Here is its formula (with the 'p' and 'a' subscripts referring to moose energy
Look back at Exercise 2 where you measured the fat stores of adult moose with wolves absent and
with wolves present vs. absent):
present, and record those values here. Note that the subscript'p' represents samples with wolves
present, while'a' represents those with wolves absent.
difference in means
xp -Xa
variability in samples
[ 1.1 ]
Mean fat stores of adult moose, wolves present ( .x ) :
In the formula above, the mean values of the two samples is given by x,and x . The variability
[ 1.2]
Mean fat stores of adult moose, wolves absent ( T. ) :_
of values within the sampled data sets is incorporated into the denominator, where 'SE' stands for
[ 13]
Calculate the difference in mean fat stores ( x, - x_):
the 'standard error of the sample-mean difference' (a fancy-sounding phrase for a simple concept
variability). Calculating this value is straightforward but requires a few steps if you are doing it 'by
Look at the following three hypothetical graphs. Each graph shows two distributions of moose fat
stores, one with wolves present (lighter gray line) and one with wolves absent (darkerline). Note that
hand'; the formula is:
in each graph, mean moose fat stores are represented by dashed vertical lines, and the difference
SE -
in means is the same for all three. However, the variation in fat stores is smaller in the distributions
on the left, and larger in those on the right
Here, var, and var, are the variances for each sample, a measure of the amount of variability in the
values. Finally, n, and n. are the number of samples in each data set. If you have never calculated
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